Global Energy Trends

INFO 523 - Project Final

The goal of this project is to analyze the complex relationships between economic and population growth, sustainable energy practices, and energy consumption

Author

Affiliation

data detectives - Ayesha, Abhishek, Sheemithra, Toluwanimi, Valerie, Alyssa

School of Information, University of Arizona

Abstract

Add project abstract here.

Question 1

Is it possible to predict a nation’s power consumption by considering its population size, gross domestic product (GDP), and the percentage of electricity generated from renewable sources and changes across the years?

Mean Squared Error: 0.000261923447842426
Mean Squared Error: 2.937309271249316e-05
Mean Squared Error: 0.0001313336189156295
Mean Squared Error (Multiple Regression): 3.203271840932969e-05
Model: Population vs Energy Consumption
R-squared: 0.3531414344090452
RMSE: 0.016184049179436708
------------------
Model: Gdp vs Energy Consumption
R-squared: 0.9306708673762983
RMSE: 0.0054196948910887185
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Model: Renewables_electricity vs Energy Consumption
R-squared: 0.6549275418223406
RMSE: 0.011460088084985625
------------------
Multiple Regression Model:
R-squared: 0.9273399797855412
RMSE: 0.005659745436795695

Repo Organization

The following folders comprise the project repository

.github/: This directory is designated for files associated with GitHub, encompassing workflows, actions, and templates tailored for issues.
_extra/: Reserved for miscellaneous files that don’t neatly fit into other project categories, providing a catch-all space for various supplementary documents.
_freeze/: Within this directory lie frozen environment files containing comprehensive information regarding the project’s environment configuration and dependencies.
data/: Specifically allocated for storing i data files crucial for the project’s functionality, encompassing input files, datasets, and other essential data resources.
images/: Serving as a repository for visual assets employed throughout the project, including diagrams, charts, and screenshots, this directory maintains visual elements integral to project documentation and presentation.
.gitignore: This file functions to specify exclusions from version control, ensuring that designated files and directories remain untracked by Git, thus streamlining the versioning process.
README.md: Serving as the primary hub of project information, this README document furnishes essential details encompassing project setup, usage instructions, and an overarching overview of project objectives and scope.
_quarto.yml: Acting as a pivotal configuration file for Quarto, this document encapsulates various settings and options governing the construction and rendering of Quarto documents, facilitating customization and control over document output.
about.qmd: This Quarto Markdown file supplements project documentation by providing additional contextual information, elucidating project purpose, contributor insights, and other pertinent project details.
index.qmd: index.qmd: This serves as the main documentation page for our project. This Quarto Markdown file provides detailed descriptions of our project, including all code and visualization .

--- title: "Global Energy Trends" subtitle: "INFO 523 - Project Final" author: - name: "data detectives - Ayesha, Abhishek, Sheemithra, Toluwanimi, Valerie, Alyssa" affiliations: - name: "School of Information, University of Arizona" description: "The goal of this project is to analyze the complex relationships between economic and population growth, sustainable energy practices, and energy consumption" format: html: code-tools: true code-overflow: wrap embed-resources: true editor: visual execute: warning: false echo: false jupyter: python3 --- ## Abstract Add project abstract here. # Question 1 Is it possible to predict a nation's power consumption by considering its population size, gross domestic product (GDP), and the percentage of electricity generated from renewable sources and changes across the years? ```{python} #| label: question #| echo: false #cell library import pandas as pd # Load the dataset data = pd.read_csv('data/owid-energy-data.csv') #columns to keep keep = (['year', 'population', 'gdp', 'electricity_generation', 'primary_energy_consumption', 'renewables_electricity']) #data for q1 q1_data = data[keep] # Drop rows with any empty values q1_data_cleaned = q1_data.dropna() # Save the cleaned dataset to a new CSV file q1_data_cleaned.to_csv('data/q1_energy_data_cleaned.csv', index=False) ``` ```{python} #| label: question1 #| echo: false # Load the dataset data = pd.read_csv('data/q1_energy_data_cleaned.csv') # Calculate the percentage of electricity generated from renewable sources data['renewables_percentage'] = (data['renewables_electricity'] / data['electricity_generation']) * 100 # Standardize or normalize the relevant columns (if needed) # Since the columns are already of type 'double', normalization might be beneficial # Here's an example of Min-Max normalization # You can choose to apply normalization to specific columns as needed columns_to_normalize = ['electricity_generation', 'primary_energy_consumption', 'renewables_electricity'] for column in columns_to_normalize: data[column] = (data[column] - data[column].min()) / (data[column].max() - data[column].min()) # Save the updated dataset to a new CSV file data.to_csv('data/q1_energy_data_processed.csv', index=False) ``` ```{python} #| label: question2 #| echo: false import matplotlib.pyplot as plt import seaborn as sns # Load the dataset data = pd.read_csv('data/q1_energy_data_processed.csv') plt.figure(figsize=(10, 8)) sns.histplot(data['primary_energy_consumption'], kde=True) plt.title('Distribution of Target Variable "primary_energy_consumption"') plt.xlabel('Primary Energy Consumption') plt.ylabel('Frequency') plt.xlim(0, 0.065) # Set x-axis limits plt.ylim(0, 90) # Set y-axis limits plt.show() ``` ```{python} #| label: question3 #| echo: false import matplotlib.pyplot as plt import seaborn as sns # Load the dataset data = pd.read_csv('data/q1_energy_data_processed.csv') # Plotting pairplot of relevant columns sns.pairplot(data[['population', 'gdp', 'renewables_electricity', 'primary_energy_consumption']]) plt.title('Pairplot of Features and Target Variable') plt.show() # Plotting correlation matrix plt.figure(figsize=(10, 8)) sns.heatmap(data[['population', 'gdp', 'renewables_electricity', 'primary_energy_consumption']].corr(), annot=True, cmap='coolwarm', fmt=".2f") plt.title('Correlation Matrix') plt.show() # Plotting scatter plots of features against target variable plt.figure(figsize=(15, 10)) # Loop through each feature and plot against the target variable for i, column in enumerate(['population', 'gdp', 'renewables_electricity']): plt.subplot(2, 2, i+1) sns.scatterplot(x=column, y='primary_energy_consumption', data=data) plt.title(f'Scatter plot of {column} vs Primary Energy Consumption') plt.xlabel(column) plt.ylabel('Primary Energy Consumption') plt.tight_layout() plt.show() ``` ```{python} #| label: question4 #| echo: false #| warning: false import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.tsa.seasonal import seasonal_decompose from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error # Load the dataset q1_energy_data = pd.read_csv('data/q1_energy_data_processed.csv') # Time Series Analysis # Decompose time series data to analyze trends, seasonality, and residuals decomposition = seasonal_decompose(q1_energy_data['primary_energy_consumption'], model='additive', period=1) decomposition.plot() plt.title('Time Series Decomposition') plt.show() # Regression Analysis # Function to perform linear regression and plot the results def perform_linear_regression(x, y, x_label): x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(x_train.values.reshape(-1, 1), y_train) y_pred = model.predict(x_test.values.reshape(-1, 1)) mse = mean_squared_error(y_test, y_pred) plt.scatter(x_test, y_test, color='blue', label='Actual') plt.plot(x_test, y_pred, color='red', label='Predicted') plt.title(f'Simple Linear Regression: {x_label} vs Primary Energy Consumption') plt.xlabel(x_label) plt.ylabel('Primary Energy Consumption') plt.legend() plt.show() print(f'Mean Squared Error: {mse}') # Perform linear regression for each independent variable for column in ['population', 'gdp', 'renewables_electricity']: perform_linear_regression(q1_energy_data[column], q1_energy_data['primary_energy_consumption'], column) # Multiple Regression # Combine multiple independent variables and perform regression X = q1_energy_data[['population', 'gdp', 'renewables_electricity']] y = q1_energy_data['primary_energy_consumption'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print(f'Mean Squared Error (Multiple Regression): {mse}') #### # Line Graphs for Time Series Data plt.figure(figsize=(15, 10)) plt.subplot(2, 2, 1) plt.plot(q1_energy_data['year'], q1_energy_data['primary_energy_consumption'], marker='o', color='blue') plt.title('Primary Energy Consumption Over Time') plt.xlabel('Year') plt.ylabel('Primary Energy Consumption') plt.subplot(2, 2, 2) plt.plot(q1_energy_data['year'], q1_energy_data['population'], marker='o', color='green') plt.title('Population Growth Over Time') plt.xlabel('Year') plt.ylabel('Population') plt.subplot(2, 2, 3) plt.plot(q1_energy_data['year'], q1_energy_data['gdp'], marker='o', color='red') plt.title('GDP Over Time') plt.xlabel('Year') plt.ylabel('GDP') plt.subplot(2, 2, 4) plt.plot(q1_energy_data['year'], q1_energy_data['renewables_electricity'], marker='o', color='orange') plt.title('Renewable Electricity Over Time') plt.xlabel('Year') plt.ylabel('Renewable Electricity') plt.tight_layout() plt.show() # Scatterplots plt.figure(figsize=(15, 5)) plt.subplot(1, 3, 1) plt.scatter(q1_energy_data['population'], q1_energy_data['primary_energy_consumption'], color='blue') plt.title('Population vs Energy Consumption') plt.xlabel('Population') plt.ylabel('Primary Energy Consumption') plt.subplot(1, 3, 2) plt.scatter(q1_energy_data['gdp'], q1_energy_data['primary_energy_consumption'], color='green') plt.title('GDP vs Energy Consumption') plt.xlabel('GDP') plt.ylabel('Primary Energy Consumption') plt.subplot(1, 3, 3) plt.scatter(q1_energy_data['renewables_electricity'], q1_energy_data['primary_energy_consumption'], color='red') plt.title('Renewable Electricity vs Energy Consumption') plt.xlabel('Renewable Electricity') plt.ylabel('Primary Energy Consumption') plt.tight_layout() plt.show() # Model Evaluation # Metrics for Simple Linear Regression # R-squared and RMSE def evaluate_model(true, pred): r_squared = sm.OLS(true, pred).fit().rsquared rmse = mean_squared_error(true, pred, squared=False) return r_squared, rmse # Evaluate each simple linear regression model for column in ['population', 'gdp', 'renewables_electricity']: X = q1_energy_data[column].values.reshape(-1, 1) y = q1_energy_data['primary_energy_consumption'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) r_squared, rmse = evaluate_model(y_test, y_pred) print(f'Model: {column.capitalize()} vs Energy Consumption') print(f'R-squared: {r_squared}') print(f'RMSE: {rmse}') print('------------------') # Metrics for Multiple Regression X = q1_energy_data[['population', 'gdp', 'renewables_electricity']] y = q1_energy_data['primary_energy_consumption'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) r_squared, rmse = evaluate_model(y_test, y_pred) print('Multiple Regression Model:') print(f'R-squared: {r_squared}') print(f'RMSE: {rmse}') ``` # Repo Organization The following folders comprise the project repository - **.github/:** This directory is designated for files associated with GitHub, encompassing workflows, actions, and templates tailored for issues. - **\_extra/:** Reserved for miscellaneous files that don't neatly fit into other project categories, providing a catch-all space for various supplementary documents. - **\_freeze/:** Within this directory lie frozen environment files containing comprehensive information regarding the project's environment configuration and dependencies. - **data/:** Specifically allocated for storing i data files crucial for the project's functionality, encompassing input files, datasets, and other essential data resources. - **images/:** Serving as a repository for visual assets employed throughout the project, including diagrams, charts, and screenshots, this directory maintains visual elements integral to project documentation and presentation. - **.gitignore:** This file functions to specify exclusions from version control, ensuring that designated files and directories remain untracked by Git, thus streamlining the versioning process. - **README.md:** Serving as the primary hub of project information, this README document furnishes essential details encompassing project setup, usage instructions, and an overarching overview of project objectives and scope. - **\_quarto.yml:** Acting as a pivotal configuration file for Quarto, this document encapsulates various settings and options governing the construction and rendering of Quarto documents, facilitating customization and control over document output. - **about.qmd:** This Quarto Markdown file supplements project documentation by providing additional contextual information, elucidating project purpose, contributor insights, and other pertinent project details. - **index.qmd:** index.qmd: This serves as the main documentation page for our project. This Quarto Markdown file provides detailed descriptions of our project, including all code and visualization .